HELIOS: From midnight to noon, continuous outdoor urban scene relighting

2026-09-01

AI Summary

为解决驾驶图像照明修改难题，HELIOS提出一种基于无标签真实数据的新方法，通过整合反照率条件和循环一致扩散管道等技术，实现日夜连续场景照明调整。

Abstract

Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely on synthetic data or paired multi-illumination supervision, which limits their generalization to the diverse and challenging conditions of real-world scenarios. To address this, we propose HELIOS, a novel image relighting approach that relies on unlabeled real-world datasets without requiring any paired images for training. Our approach integrates albedo-based conditioning into a cycle-consistent diffusion pipeline to prevent identity collapse and ensure accurate domain translation. To handle low-visibility nighttime conditions, we introduce a robust albedo distillation strategy that transfers structural stability from the daytime domain. Additionally, we replace traditional text prompts with a fine-grained control mechanism based on GPS-derived solar angles, enabling smooth and continuous lighting manipulation across the day-night cycle. Through extensive evaluation and a user study, we demonstrate that HELIOS produces structurally consistent and realistic results in both night-to-day and day-to-night tasks, outperforming state-of-the-art methods.

Problem

• image relighting
• driving images
• illumination modification
• real-world scenarios
• unlabeled real-world datasets

Innovation

• albedo-based conditioning
• cycle-consistent diffusion pipeline
• robust albedo distillation
• GPS-derived solar angles
• fine-grained control mechanism